Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory369.2 B

Variable types

Text1
Numeric13
Categorical5

Alerts

activity_score is highly overall correlated with click_through_rateHigh correlation
avg_cart_value is highly overall correlated with pca1High correlation
click_through_rate is highly overall correlated with activity_scoreHigh correlation
cluster is highly overall correlated with purchase_frequency_log and 1 other fieldsHigh correlation
days_since_last_login is highly overall correlated with recency_valueHigh correlation
pca1 is highly overall correlated with avg_cart_value and 1 other fieldsHigh correlation
pca2 is highly overall correlated with used_discountHigh correlation
purchase_frequency is highly overall correlated with purchase_frequency_log and 1 other fieldsHigh correlation
purchase_frequency_log is highly overall correlated with cluster and 2 other fieldsHigh correlation
recency_value is highly overall correlated with days_since_last_login and 1 other fieldsHigh correlation
used_discount is highly overall correlated with cluster and 1 other fieldsHigh correlation
value_score is highly overall correlated with purchase_frequency and 1 other fieldsHigh correlation
user_id has unique values Unique
pca1 has unique values Unique
pca2 has unique values Unique
num_categories_browsed has 168 (1.7%) zeros Zeros
days_since_last_login has 104 (1.0%) zeros Zeros
recency_value has 104 (1.0%) zeros Zeros

Reproduction

Analysis started2025-04-14 16:55:10.449974
Analysis finished2025-04-14 16:55:23.756399
Duration13.31 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

user_id
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.7 KiB
2025-04-14T22:25:24.009779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowUSER00000
2nd rowUSER00001
3rd rowUSER00002
4th rowUSER00003
5th rowUSER00004
ValueCountFrequency (%)
user00000 1
 
< 0.1%
user00008 1
 
< 0.1%
user00017 1
 
< 0.1%
user00002 1
 
< 0.1%
user00003 1
 
< 0.1%
user00004 1
 
< 0.1%
user00005 1
 
< 0.1%
user00006 1
 
< 0.1%
user00007 1
 
< 0.1%
user00009 1
 
< 0.1%
Other values (9990) 9990
99.9%
2025-04-14T22:25:24.362222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14000
15.6%
U 10000
11.1%
S 10000
11.1%
E 10000
11.1%
R 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14000
15.6%
U 10000
11.1%
S 10000
11.1%
E 10000
11.1%
R 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14000
15.6%
U 10000
11.1%
S 10000
11.1%
E 10000
11.1%
R 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14000
15.6%
U 10000
11.1%
S 10000
11.1%
E 10000
11.1%
R 10000
11.1%
6 4000
 
4.4%
7 4000
 
4.4%
3 4000
 
4.4%
4 4000
 
4.4%
5 4000
 
4.4%
Other values (4) 16000
17.8%

num_product_views
Real number (ℝ)

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0073
Minimum3
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:25:24.457991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q112
median15
Q317
95-th percentile22
Maximum30
Range27
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8671362
Coefficient of variation (CV)0.25768367
Kurtosis0.019416639
Mean15.0073
Median Absolute Deviation (MAD)3
Skewness0.23227361
Sum150073
Variance14.954742
MonotonicityNot monotonic
2025-04-14T22:25:24.538094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
15 1069
10.7%
14 1027
10.3%
16 971
9.7%
13 931
9.3%
17 841
8.4%
12 835
8.3%
18 716
 
7.2%
11 665
 
6.7%
19 540
 
5.4%
10 477
 
4.8%
Other values (18) 1928
19.3%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 10
 
0.1%
5 26
 
0.3%
6 52
 
0.5%
7 97
 
1.0%
8 196
 
2.0%
9 303
 
3.0%
10 477
4.8%
11 665
6.7%
12 835
8.3%
ValueCountFrequency (%)
30 1
 
< 0.1%
29 4
 
< 0.1%
28 6
 
0.1%
27 16
 
0.2%
26 30
 
0.3%
25 46
 
0.5%
24 90
 
0.9%
23 154
1.5%
22 220
2.2%
21 274
2.7%

avg_view_time
Real number (ℝ)

Distinct5550
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.735054
Minimum-28.1
Maximum118.83
Zeros0
Zeros (%)0.0%
Negative116
Negative (%)1.2%
Memory size78.2 KiB
2025-04-14T22:25:24.617766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-28.1
5-th percentile12.6595
Q131
median44.875
Q358.26
95-th percentile77.6705
Maximum118.83
Range146.93
Interquartile range (IQR)27.26

Descriptive statistics

Standard deviation19.811203
Coefficient of variation (CV)0.44285636
Kurtosis-0.077638929
Mean44.735054
Median Absolute Deviation (MAD)13.615
Skewness0.0056993562
Sum447350.54
Variance392.48377
MonotonicityNot monotonic
2025-04-14T22:25:24.708860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.82 8
 
0.1%
54.74 8
 
0.1%
38.31 7
 
0.1%
55.39 7
 
0.1%
34.91 7
 
0.1%
48.82 7
 
0.1%
39.92 7
 
0.1%
27.88 7
 
0.1%
44.95 7
 
0.1%
41.72 7
 
0.1%
Other values (5540) 9928
99.3%
ValueCountFrequency (%)
-28.1 1
< 0.1%
-24.9 1
< 0.1%
-24.07 1
< 0.1%
-23.55 1
< 0.1%
-20.91 1
< 0.1%
-19.26 1
< 0.1%
-18.99 1
< 0.1%
-18.18 1
< 0.1%
-16.86 1
< 0.1%
-16.11 2
< 0.1%
ValueCountFrequency (%)
118.83 1
< 0.1%
117.23 1
< 0.1%
117.06 1
< 0.1%
112.09 1
< 0.1%
109.31 1
< 0.1%
108.18 1
< 0.1%
107.73 1
< 0.1%
107.65 1
< 0.1%
105.77 1
< 0.1%
105.28 1
< 0.1%

click_through_rate
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.283625
Minimum0
Maximum0.86
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:25:24.827726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.16
median0.26
Q30.39
95-th percentile0.58
Maximum0.86
Range0.86
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.15899798
Coefficient of variation (CV)0.56059226
Kurtosis-0.070832175
Mean0.283625
Median Absolute Deviation (MAD)0.11
Skewness0.61098431
Sum2836.25
Variance0.025280357
MonotonicityNot monotonic
2025-04-14T22:25:24.934119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.19 271
 
2.7%
0.18 263
 
2.6%
0.22 257
 
2.6%
0.2 257
 
2.6%
0.15 250
 
2.5%
0.26 245
 
2.5%
0.17 242
 
2.4%
0.27 241
 
2.4%
0.12 240
 
2.4%
0.21 232
 
2.3%
Other values (75) 7502
75.0%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.01 33
 
0.3%
0.02 52
 
0.5%
0.03 76
 
0.8%
0.04 100
1.0%
0.05 132
1.3%
0.06 156
1.6%
0.07 164
1.6%
0.08 159
1.6%
0.09 195
1.9%
ValueCountFrequency (%)
0.86 1
 
< 0.1%
0.84 3
 
< 0.1%
0.83 4
 
< 0.1%
0.82 7
0.1%
0.81 3
 
< 0.1%
0.79 5
0.1%
0.78 6
0.1%
0.77 5
0.1%
0.76 6
0.1%
0.75 12
0.1%

avg_cart_value
Real number (ℝ)

High correlation 

Distinct6568
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.013534
Minimum-37.14
Maximum198.01
Zeros0
Zeros (%)0.0%
Negative69
Negative (%)0.7%
Memory size78.2 KiB
2025-04-14T22:25:25.025057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-37.14
5-th percentile24.3995
Q154.69
median75.19
Q395.52
95-th percentile124.0815
Maximum198.01
Range235.15
Interquartile range (IQR)40.83

Descriptive statistics

Standard deviation30.128907
Coefficient of variation (CV)0.40164628
Kurtosis0.018895359
Mean75.013534
Median Absolute Deviation (MAD)20.44
Skewness-0.016110477
Sum750135.34
Variance907.75103
MonotonicityNot monotonic
2025-04-14T22:25:25.115179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.6 6
 
0.1%
79.85 6
 
0.1%
73.26 6
 
0.1%
88.81 6
 
0.1%
99.69 6
 
0.1%
68.47 6
 
0.1%
76.94 6
 
0.1%
54.86 6
 
0.1%
82 5
 
0.1%
96.33 5
 
0.1%
Other values (6558) 9942
99.4%
ValueCountFrequency (%)
-37.14 1
< 0.1%
-36.4 1
< 0.1%
-30.98 1
< 0.1%
-22.64 1
< 0.1%
-19.19 1
< 0.1%
-18.39 1
< 0.1%
-17.36 1
< 0.1%
-16.96 1
< 0.1%
-16.49 1
< 0.1%
-16.41 1
< 0.1%
ValueCountFrequency (%)
198.01 1
< 0.1%
192.48 1
< 0.1%
186.49 1
< 0.1%
182.77 1
< 0.1%
182.76 1
< 0.1%
179.53 1
< 0.1%
177.81 1
< 0.1%
176.08 1
< 0.1%
175.41 1
< 0.1%
175.31 1
< 0.1%

purchase_frequency
Real number (ℝ)

High correlation 

Distinct712
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.482123
Minimum0
Maximum13.34
Zeros34
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:25:25.203114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.08
Q10.43
median1.04
Q32.05
95-th percentile4.41
Maximum13.34
Range13.34
Interquartile range (IQR)1.62

Descriptive statistics

Standard deviation1.468427
Coefficient of variation (CV)0.9907592
Kurtosis5.4860824
Mean1.482123
Median Absolute Deviation (MAD)0.71
Skewness1.9442973
Sum14821.23
Variance2.1562778
MonotonicityNot monotonic
2025-04-14T22:25:25.303166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 74
 
0.7%
0.01 73
 
0.7%
0.1 72
 
0.7%
0.36 69
 
0.7%
0.32 67
 
0.7%
0.28 67
 
0.7%
0.18 65
 
0.7%
0.09 65
 
0.7%
0.12 64
 
0.6%
0.08 64
 
0.6%
Other values (702) 9320
93.2%
ValueCountFrequency (%)
0 34
0.3%
0.01 73
0.7%
0.02 74
0.7%
0.03 58
0.6%
0.04 53
0.5%
0.05 60
0.6%
0.06 63
0.6%
0.07 56
0.6%
0.08 64
0.6%
0.09 65
0.7%
ValueCountFrequency (%)
13.34 1
< 0.1%
12.6 1
< 0.1%
11.85 1
< 0.1%
11.58 1
< 0.1%
11.5 1
< 0.1%
11.37 1
< 0.1%
11.14 1
< 0.1%
10.4 1
< 0.1%
10.35 1
< 0.1%
10.21 1
< 0.1%

num_categories_browsed
Real number (ℝ)

Zeros 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0231
Minimum0
Maximum13
Zeros168
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:25:25.385101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile8
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.001791
Coefficient of variation (CV)0.49757425
Kurtosis0.13952934
Mean4.0231
Median Absolute Deviation (MAD)1
Skewness0.49484561
Sum40231
Variance4.0071671
MonotonicityNot monotonic
2025-04-14T22:25:25.457739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 2033
20.3%
4 1941
19.4%
5 1535
15.3%
2 1419
14.2%
6 993
9.9%
1 723
 
7.2%
7 658
 
6.6%
8 314
 
3.1%
0 168
 
1.7%
9 136
 
1.4%
Other values (4) 80
 
0.8%
ValueCountFrequency (%)
0 168
 
1.7%
1 723
 
7.2%
2 1419
14.2%
3 2033
20.3%
4 1941
19.4%
5 1535
15.3%
6 993
9.9%
7 658
 
6.6%
8 314
 
3.1%
9 136
 
1.4%
ValueCountFrequency (%)
13 2
 
< 0.1%
12 4
 
< 0.1%
11 18
 
0.2%
10 56
 
0.6%
9 136
 
1.4%
8 314
 
3.1%
7 658
 
6.6%
6 993
9.9%
5 1535
15.3%
4 1941
19.4%

used_discount
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
0
6010 
1
3990 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 6010
60.1%
1 3990
39.9%

Length

2025-04-14T22:25:25.532492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:25:25.600360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6010
60.1%
1 3990
39.9%

Most occurring characters

ValueCountFrequency (%)
0 6010
60.1%
1 3990
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6010
60.1%
1 3990
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6010
60.1%
1 3990
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6010
60.1%
1 3990
39.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size604.7 KiB
Free
5007 
Silver
2577 
Gold
1433 
Platinum
983 

Length

Max length8
Median length4
Mean length4.9086
Min length4

Characters and Unicode

Total characters49086
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSilver
2nd rowGold
3rd rowFree
4th rowSilver
5th rowFree

Common Values

ValueCountFrequency (%)
Free 5007
50.1%
Silver 2577
25.8%
Gold 1433
 
14.3%
Platinum 983
 
9.8%

Length

2025-04-14T22:25:25.677858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:25:25.749551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
free 5007
50.1%
silver 2577
25.8%
gold 1433
 
14.3%
platinum 983
 
9.8%

Most occurring characters

ValueCountFrequency (%)
e 12591
25.7%
r 7584
15.5%
F 5007
 
10.2%
l 4993
 
10.2%
i 3560
 
7.3%
S 2577
 
5.2%
v 2577
 
5.2%
G 1433
 
2.9%
o 1433
 
2.9%
d 1433
 
2.9%
Other values (6) 5898
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12591
25.7%
r 7584
15.5%
F 5007
 
10.2%
l 4993
 
10.2%
i 3560
 
7.3%
S 2577
 
5.2%
v 2577
 
5.2%
G 1433
 
2.9%
o 1433
 
2.9%
d 1433
 
2.9%
Other values (6) 5898
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12591
25.7%
r 7584
15.5%
F 5007
 
10.2%
l 4993
 
10.2%
i 3560
 
7.3%
S 2577
 
5.2%
v 2577
 
5.2%
G 1433
 
2.9%
o 1433
 
2.9%
d 1433
 
2.9%
Other values (6) 5898
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12591
25.7%
r 7584
15.5%
F 5007
 
10.2%
l 4993
 
10.2%
i 3560
 
7.3%
S 2577
 
5.2%
v 2577
 
5.2%
G 1433
 
2.9%
o 1433
 
2.9%
d 1433
 
2.9%
Other values (6) 5898
12.0%

device_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size618.2 KiB
Mobile
6060 
Desktop
2937 
Tablet
1003 

Length

Max length7
Median length6
Mean length6.2937
Min length6

Characters and Unicode

Total characters62937
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile
2nd rowMobile
3rd rowMobile
4th rowDesktop
5th rowMobile

Common Values

ValueCountFrequency (%)
Mobile 6060
60.6%
Desktop 2937
29.4%
Tablet 1003
 
10.0%

Length

2025-04-14T22:25:25.826928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:25:25.891249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mobile 6060
60.6%
desktop 2937
29.4%
tablet 1003
 
10.0%

Most occurring characters

ValueCountFrequency (%)
e 10000
15.9%
o 8997
14.3%
b 7063
11.2%
l 7063
11.2%
M 6060
9.6%
i 6060
9.6%
t 3940
 
6.3%
D 2937
 
4.7%
s 2937
 
4.7%
k 2937
 
4.7%
Other values (3) 4943
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62937
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10000
15.9%
o 8997
14.3%
b 7063
11.2%
l 7063
11.2%
M 6060
9.6%
i 6060
9.6%
t 3940
 
6.3%
D 2937
 
4.7%
s 2937
 
4.7%
k 2937
 
4.7%
Other values (3) 4943
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62937
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10000
15.9%
o 8997
14.3%
b 7063
11.2%
l 7063
11.2%
M 6060
9.6%
i 6060
9.6%
t 3940
 
6.3%
D 2937
 
4.7%
s 2937
 
4.7%
k 2937
 
4.7%
Other values (3) 4943
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62937
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10000
15.9%
o 8997
14.3%
b 7063
11.2%
l 7063
11.2%
M 6060
9.6%
i 6060
9.6%
t 3940
 
6.3%
D 2937
 
4.7%
s 2937
 
4.7%
k 2937
 
4.7%
Other values (3) 4943
7.9%

referral_channel
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size605.5 KiB
Direct
2548 
Email
2530 
Ads
2512 
Social
2410 

Length

Max length6
Median length5
Mean length4.9934
Min length3

Characters and Unicode

Total characters49934
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmail
2nd rowDirect
3rd rowEmail
4th rowDirect
5th rowAds

Common Values

ValueCountFrequency (%)
Direct 2548
25.5%
Email 2530
25.3%
Ads 2512
25.1%
Social 2410
24.1%

Length

2025-04-14T22:25:25.966894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:25:26.035895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
direct 2548
25.5%
email 2530
25.3%
ads 2512
25.1%
social 2410
24.1%

Most occurring characters

ValueCountFrequency (%)
i 7488
15.0%
c 4958
9.9%
a 4940
 
9.9%
l 4940
 
9.9%
D 2548
 
5.1%
r 2548
 
5.1%
e 2548
 
5.1%
t 2548
 
5.1%
E 2530
 
5.1%
m 2530
 
5.1%
Other values (5) 12356
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 7488
15.0%
c 4958
9.9%
a 4940
 
9.9%
l 4940
 
9.9%
D 2548
 
5.1%
r 2548
 
5.1%
e 2548
 
5.1%
t 2548
 
5.1%
E 2530
 
5.1%
m 2530
 
5.1%
Other values (5) 12356
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 7488
15.0%
c 4958
9.9%
a 4940
 
9.9%
l 4940
 
9.9%
D 2548
 
5.1%
r 2548
 
5.1%
e 2548
 
5.1%
t 2548
 
5.1%
E 2530
 
5.1%
m 2530
 
5.1%
Other values (5) 12356
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 7488
15.0%
c 4958
9.9%
a 4940
 
9.9%
l 4940
 
9.9%
D 2548
 
5.1%
r 2548
 
5.1%
e 2548
 
5.1%
t 2548
 
5.1%
E 2530
 
5.1%
m 2530
 
5.1%
Other values (5) 12356
24.7%

days_since_last_login
Real number (ℝ)

High correlation  Zeros 

Distinct90
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.4047
Minimum0
Maximum89
Zeros104
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:25:26.116870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q122
median45
Q367
95-th percentile85
Maximum89
Range89
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.041542
Coefficient of variation (CV)0.58645915
Kurtosis-1.204091
Mean44.4047
Median Absolute Deviation (MAD)23
Skewness-0.00024062479
Sum444047
Variance678.16193
MonotonicityNot monotonic
2025-04-14T22:25:26.205639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 141
 
1.4%
6 134
 
1.3%
46 130
 
1.3%
18 129
 
1.3%
20 128
 
1.3%
72 128
 
1.3%
79 128
 
1.3%
51 126
 
1.3%
14 125
 
1.2%
2 125
 
1.2%
Other values (80) 8706
87.1%
ValueCountFrequency (%)
0 104
1.0%
1 115
1.1%
2 125
1.2%
3 141
1.4%
4 97
1.0%
5 91
0.9%
6 134
1.3%
7 121
1.2%
8 103
1.0%
9 108
1.1%
ValueCountFrequency (%)
89 115
1.1%
88 114
1.1%
87 106
1.1%
86 101
1.0%
85 103
1.0%
84 110
1.1%
83 100
1.0%
82 116
1.2%
81 123
1.2%
80 118
1.2%

purchase_frequency_log
Real number (ℝ)

High correlation 

Distinct712
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77169651
Minimum0
Maximum2.6630528
Zeros34
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:25:26.293475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.076961041
Q10.35767444
median0.71294981
Q31.1151416
95-th percentile1.6882491
Maximum2.6630528
Range2.6630528
Interquartile range (IQR)0.75746715

Descriptive statistics

Standard deviation0.5042743
Coefficient of variation (CV)0.65346195
Kurtosis-0.34806393
Mean0.77169651
Median Absolute Deviation (MAD)0.37647757
Skewness0.54430549
Sum7716.9651
Variance0.25429257
MonotonicityNot monotonic
2025-04-14T22:25:26.385447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0198026273 74
 
0.7%
0.009950330853 73
 
0.7%
0.0953101798 72
 
0.7%
0.3074846997 69
 
0.7%
0.2776317366 67
 
0.7%
0.2468600779 67
 
0.7%
0.1655144385 65
 
0.7%
0.08617769624 65
 
0.7%
0.1133286853 64
 
0.6%
0.07696104114 64
 
0.6%
Other values (702) 9320
93.2%
ValueCountFrequency (%)
0 34
0.3%
0.009950330853 73
0.7%
0.0198026273 74
0.7%
0.02955880224 58
0.6%
0.03922071315 53
0.5%
0.04879016417 60
0.6%
0.05826890812 63
0.6%
0.06765864847 56
0.6%
0.07696104114 64
0.6%
0.08617769624 65
0.7%
ValueCountFrequency (%)
2.663052835 1
< 0.1%
2.610069793 1
< 0.1%
2.553343811 1
< 0.1%
2.532108251 1
< 0.1%
2.525728644 1
< 0.1%
2.515274186 1
< 0.1%
2.496505786 1
< 0.1%
2.433613355 1
< 0.1%
2.429217744 1
< 0.1%
2.416806237 1
< 0.1%

pca1
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5171865 × 10-17
Minimum-4.2820098
Maximum4.1138906
Zeros0
Zeros (%)0.0%
Negative4922
Negative (%)49.2%
Memory size78.2 KiB
2025-04-14T22:25:26.474998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-4.2820098
5-th percentile-1.7163556
Q1-0.65152677
median0.021910895
Q30.68608895
95-th percentile1.6314128
Maximum4.1138906
Range8.3959004
Interquartile range (IQR)1.3376157

Descriptive statistics

Standard deviation1.0181841
Coefficient of variation (CV)2.8948823 × 1016
Kurtosis0.27596645
Mean3.5171865 × 10-17
Median Absolute Deviation (MAD)0.6692167
Skewness-0.18430923
Sum4.6895821 × 10-13
Variance1.0366989
MonotonicityNot monotonic
2025-04-14T22:25:26.563524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2524497696 1
 
< 0.1%
0.7870053262 1
 
< 0.1%
0.04136595508 1
 
< 0.1%
0.8276707766 1
 
< 0.1%
0.2693683477 1
 
< 0.1%
-0.5617937643 1
 
< 0.1%
-0.496489803 1
 
< 0.1%
-0.6589629232 1
 
< 0.1%
2.18882271 1
 
< 0.1%
1.328372686 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-4.282009793 1
< 0.1%
-4.194176847 1
< 0.1%
-4.104924828 1
< 0.1%
-4.082828931 1
< 0.1%
-3.841577149 1
< 0.1%
-3.766319152 1
< 0.1%
-3.745874675 1
< 0.1%
-3.659295957 1
< 0.1%
-3.620957197 1
< 0.1%
-3.604030943 1
< 0.1%
ValueCountFrequency (%)
4.113890557 1
< 0.1%
3.56413485 1
< 0.1%
3.525094792 1
< 0.1%
3.421840348 1
< 0.1%
3.164637781 1
< 0.1%
3.16181585 1
< 0.1%
3.104412087 1
< 0.1%
3.037106702 1
< 0.1%
3.016008468 1
< 0.1%
2.969210842 1
< 0.1%

pca2
Real number (ℝ)

High correlation  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.405365 × 10-17
Minimum-4.1586571
Maximum3.2817225
Zeros0
Zeros (%)0.0%
Negative5063
Negative (%)50.6%
Memory size78.2 KiB
2025-04-14T22:25:26.649987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-4.1586571
5-th percentile-1.6238518
Q1-0.70534042
median-0.017598331
Q30.72316377
95-th percentile1.6639542
Maximum3.2817225
Range7.4403796
Interquartile range (IQR)1.4285042

Descriptive statistics

Standard deviation1.011508
Coefficient of variation (CV)-2.2960822 × 1016
Kurtosis-0.14044116
Mean-4.405365 × 10-17
Median Absolute Deviation (MAD)0.71083359
Skewness-0.050560626
Sum-3.4106051 × 10-13
Variance1.0231484
MonotonicityNot monotonic
2025-04-14T22:25:26.742796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6786343775 1
 
< 0.1%
1.140788174 1
 
< 0.1%
-1.090986361 1
 
< 0.1%
-0.0774933944 1
 
< 0.1%
-0.13842774 1
 
< 0.1%
-0.1218399028 1
 
< 0.1%
0.138882741 1
 
< 0.1%
-0.8372976166 1
 
< 0.1%
-1.357500295 1
 
< 0.1%
1.81281355 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-4.158657131 1
< 0.1%
-4.075586499 1
< 0.1%
-4.017278731 1
< 0.1%
-3.919389281 1
< 0.1%
-3.802494286 1
< 0.1%
-3.771553953 1
< 0.1%
-3.585138934 1
< 0.1%
-3.521301191 1
< 0.1%
-3.514175035 1
< 0.1%
-3.473415986 1
< 0.1%
ValueCountFrequency (%)
3.281722456 1
< 0.1%
3.019187746 1
< 0.1%
2.984976647 1
< 0.1%
2.846219087 1
< 0.1%
2.827805907 1
< 0.1%
2.81097217 1
< 0.1%
2.802153726 1
< 0.1%
2.770489467 1
< 0.1%
2.754524227 1
< 0.1%
2.724362252 1
< 0.1%

cluster
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.5 KiB
3
3617 
1
2675 
0
2653 
2
1055 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
3 3617
36.2%
1 2675
26.8%
0 2653
26.5%
2 1055
 
10.5%

Length

2025-04-14T22:25:26.826944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T22:25:26.889779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 3617
36.2%
1 2675
26.8%
0 2653
26.5%
2 1055
 
10.5%

Most occurring characters

ValueCountFrequency (%)
3 3617
36.2%
1 2675
26.8%
0 2653
26.5%
2 1055
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 3617
36.2%
1 2675
26.8%
0 2653
26.5%
2 1055
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 3617
36.2%
1 2675
26.8%
0 2653
26.5%
2 1055
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 3617
36.2%
1 2675
26.8%
0 2653
26.5%
2 1055
 
10.5%

activity_score
Real number (ℝ)

High correlation 

Distinct795
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.257354
Minimum0
Maximum20.25
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-04-14T22:25:26.970334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.84
Q12.2175
median3.74
Q35.78
95-th percentile9.36
Maximum20.25
Range20.25
Interquartile range (IQR)3.5625

Descriptive statistics

Standard deviation2.6948507
Coefficient of variation (CV)0.63298722
Kurtosis1.2948045
Mean4.257354
Median Absolute Deviation (MAD)1.72
Skewness1.034655
Sum42573.54
Variance7.2622202
MonotonicityNot monotonic
2025-04-14T22:25:27.070428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8 86
 
0.9%
2.88 74
 
0.7%
2.4 74
 
0.7%
2.1 70
 
0.7%
4.32 69
 
0.7%
4.2 68
 
0.7%
2.7 68
 
0.7%
3.6 63
 
0.6%
4.5 63
 
0.6%
4.8 62
 
0.6%
Other values (785) 9303
93.0%
ValueCountFrequency (%)
0 3
< 0.1%
0.07 1
 
< 0.1%
0.08 2
< 0.1%
0.09 3
< 0.1%
0.1 3
< 0.1%
0.11 1
 
< 0.1%
0.12 4
< 0.1%
0.13 2
< 0.1%
0.14 4
< 0.1%
0.15 2
< 0.1%
ValueCountFrequency (%)
20.25 1
< 0.1%
18.5 1
< 0.1%
17.64 1
< 0.1%
17.25 1
< 0.1%
16.94 1
< 0.1%
16.74 1
< 0.1%
16.56 1
< 0.1%
16.5 1
< 0.1%
16.4 1
< 0.1%
16.38 2
< 0.1%

value_score
Real number (ℝ)

High correlation 

Distinct9923
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.46031
Minimum-332.403
Maximum1529.718
Zeros34
Zeros (%)0.3%
Negative69
Negative (%)0.7%
Memory size78.2 KiB
2025-04-14T22:25:27.166905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-332.403
5-th percentile3.8698
Q126.8378
median69.6423
Q3149.65023
95-th percentile356.48028
Maximum1529.718
Range1862.121
Interquartile range (IQR)122.81243

Descriptive statistics

Standard deviation123.73036
Coefficient of variation (CV)1.1201341
Kurtosis9.2654465
Mean110.46031
Median Absolute Deviation (MAD)51.55685
Skewness2.3969377
Sum1104603.1
Variance15309.202
MonotonicityNot monotonic
2025-04-14T22:25:27.259631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
 
0.3%
22.896 3
 
< 0.1%
72.3528 2
 
< 0.1%
25.872 2
 
< 0.1%
14.3902 2
 
< 0.1%
48.9744 2
 
< 0.1%
0.5263 2
 
< 0.1%
57.7362 2
 
< 0.1%
16.9116 2
 
< 0.1%
28.0984 2
 
< 0.1%
Other values (9913) 9947
99.5%
ValueCountFrequency (%)
-332.403 1
< 0.1%
-108.4456 1
< 0.1%
-96.096 1
< 0.1%
-35.6921 1
< 0.1%
-34.344 1
< 0.1%
-29.164 1
< 0.1%
-28.6032 1
< 0.1%
-28.033 1
< 0.1%
-24.8829 1
< 0.1%
-24.784 1
< 0.1%
ValueCountFrequency (%)
1529.718 1
< 0.1%
1206.9135 1
< 0.1%
1150.506 1
< 0.1%
1075.8277 1
< 0.1%
1059.6081 1
< 0.1%
1046.864 1
< 0.1%
1008.6327 1
< 0.1%
963.1776 1
< 0.1%
957.4887 1
< 0.1%
945.234 1
< 0.1%

recency_value
Real number (ℝ)

High correlation  Zeros 

Distinct9746
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3340.1151
Minimum-2896.92
Maximum16038.14
Zeros104
Zeros (%)1.0%
Negative68
Negative (%)0.7%
Memory size78.2 KiB
2025-04-14T22:25:27.349468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2896.92
5-th percentile177.69
Q11292.94
median2887.18
Q34962.025
95-th percentile8097.8685
Maximum16038.14
Range18935.06
Interquartile range (IQR)3669.085

Descriptive statistics

Standard deviation2508.3599
Coefficient of variation (CV)0.75098008
Kurtosis0.2254322
Mean3340.1151
Median Absolute Deviation (MAD)1776.9
Skewness0.79610873
Sum33401151
Variance6291869.3
MonotonicityNot monotonic
2025-04-14T22:25:27.444032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104
 
1.0%
2250 3
 
< 0.1%
3201.12 3
 
< 0.1%
1154.7 2
 
< 0.1%
103.59 2
 
< 0.1%
3400.2 2
 
< 0.1%
2593.15 2
 
< 0.1%
4460.4 2
 
< 0.1%
1118.92 2
 
< 0.1%
3829.5 2
 
< 0.1%
Other values (9736) 9876
98.8%
ValueCountFrequency (%)
-2896.92 1
< 0.1%
-2230.56 1
< 0.1%
-1601.6 1
< 0.1%
-1394.85 1
< 0.1%
-1285.73 1
< 0.1%
-1169.99 1
< 0.1%
-1086.72 1
< 0.1%
-1016.64 1
< 0.1%
-944.59 1
< 0.1%
-906.95 1
< 0.1%
ValueCountFrequency (%)
16038.14 1
< 0.1%
14734.44 1
< 0.1%
14658.3 1
< 0.1%
13691.37 1
< 0.1%
13575.48 1
< 0.1%
13214.96 1
< 0.1%
13159.44 1
< 0.1%
12814.37 1
< 0.1%
12777.6 1
< 0.1%
12753.72 1
< 0.1%

Interactions

2025-04-14T22:25:22.394783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:11.271734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.377666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.374689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.217129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.140287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.028418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.913679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.850683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.716475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.550363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.543085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.463050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.463013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:11.401415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.454667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.438582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.280358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.207329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.098248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.976282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.915899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.777427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.616876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.612235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.530722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.535580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:11.522751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.532375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.507104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.349722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.279075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.173163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.134170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.984963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.843301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.686820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.686103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.601165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.601737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:11.655927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.602322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.567403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.410381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.343211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.238147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.194837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.047316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.902173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.751095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.754746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.701151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.666140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:11.718994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.674622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.627401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.468979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.407966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.300932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.257039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.109855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.961500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.813902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.821619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.766148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.737337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:11.791719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.750314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.696125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.546839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.476802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.369050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.324587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.178698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.027827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.884345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.894794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.839748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.807043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:11.866890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.820119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.761356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.612848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.545009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.436169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.391053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.245475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.091014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.063425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.966333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.908949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.875020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:11.930083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.884519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.821340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.673294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.607738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.500093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.449737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.307117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.151603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.127348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.032915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.974697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.943636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:11.999862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.016785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.884688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.735596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.675665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.567059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.514681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.371347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.215595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.195400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.101835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.042081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:23.007614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.068061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.082296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.942883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.870389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.738002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.629711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.573027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.433109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.275053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.258648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.167118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.105731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:23.078276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.143640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.152854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.011114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.936877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.808664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.699508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.641811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.502252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.342580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.327377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.240230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.176519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:23.152893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.223049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.227804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.081810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.006426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.882883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.773349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.712738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.574277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.414593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.401224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.315421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.251838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:23.225071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:12.300580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:13.300207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:14.149051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.072810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:15.954828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:16.842400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:17.781957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:18.645875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:19.481685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:20.471433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:21.389544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-14T22:25:22.322393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-14T22:25:27.520849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
activity_scoreavg_cart_valueavg_view_timeclick_through_rateclusterdays_since_last_logindevice_typemembership_statusnum_categories_browsednum_product_viewspca1pca2purchase_frequencypurchase_frequency_logrecency_valuereferral_channelused_discountvalue_score
activity_score1.0000.0060.0030.9120.0000.0100.0320.0000.0080.3720.4510.140-0.007-0.0070.0130.0000.000-0.000
avg_cart_value0.0061.000-0.0030.0020.0190.0080.0000.0200.0090.0100.561-0.050-0.009-0.0090.4900.0070.0000.355
avg_view_time0.003-0.0031.0000.0050.030-0.0090.0000.000-0.003-0.0060.0270.370-0.007-0.007-0.0070.0000.000-0.008
click_through_rate0.9120.0020.0051.0000.0000.0090.0190.0000.0060.0010.4120.238-0.010-0.0100.0090.0120.000-0.006
cluster0.0000.0190.0300.0001.0000.4080.0130.0000.0150.0080.3190.4820.4970.5080.2990.0000.9480.384
days_since_last_login0.0100.008-0.0090.0090.4081.0000.0000.022-0.006-0.0030.477-0.308-0.005-0.0050.8220.0000.0090.002
device_type0.0320.0000.0000.0190.0130.0001.0000.0000.0000.0110.0110.0000.0130.0000.0000.0150.0000.011
membership_status0.0000.0200.0000.0000.0000.0220.0001.0000.0000.0000.0080.0080.0140.0000.0090.0000.0000.000
num_categories_browsed0.0080.009-0.0030.0060.015-0.0060.0000.0001.0000.0060.0410.2570.0050.005-0.0020.0080.0100.007
num_product_views0.3720.010-0.0060.0010.008-0.0030.0110.0000.0061.0000.191-0.1980.0070.0070.0060.0090.0000.014
pca10.4510.5610.0270.4120.3190.4770.0110.0080.0410.1911.000-0.041-0.387-0.3870.6780.0000.181-0.134
pca20.140-0.0500.3700.2380.482-0.3080.0000.0080.257-0.198-0.0411.000-0.371-0.371-0.2770.0000.647-0.354
purchase_frequency-0.007-0.009-0.007-0.0100.497-0.0050.0130.0140.0050.007-0.387-0.3711.0001.000-0.0060.0000.0000.900
purchase_frequency_log-0.007-0.009-0.007-0.0100.508-0.0050.0000.0000.0050.007-0.387-0.3711.0001.000-0.0060.0000.0000.900
recency_value0.0130.490-0.0070.0090.2990.8220.0000.009-0.0020.0060.678-0.277-0.006-0.0061.0000.0050.0000.198
referral_channel0.0000.0070.0000.0120.0000.0000.0150.0000.0080.0090.0000.0000.0000.0000.0051.0000.0000.000
used_discount0.0000.0000.0000.0000.9480.0090.0000.0000.0100.0000.1810.6470.0000.0000.0000.0001.0000.000
value_score-0.0000.355-0.008-0.0060.3840.0020.0110.0000.0070.014-0.134-0.3540.9000.9000.1980.0000.0001.000

Missing values

2025-04-14T22:25:23.471491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-14T22:25:23.666383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idnum_product_viewsavg_view_timeclick_through_rateavg_cart_valuepurchase_frequencynum_categories_browsedused_discountmembership_statusdevice_typereferral_channeldays_since_last_loginpurchase_frequency_logpca1pca2clusteractivity_scorevalue_scorerecency_value
0USER000001854.010.23103.571.2531SilverMobileEmail370.8109300.2524500.67863434.14129.46253832.09
1USER000011034.410.2393.501.2351GoldMobileDirect500.802002-0.0815200.84670132.30115.00504675.00
2USER000021630.050.4180.260.9671FreeMobileEmail120.672944-0.1164901.55226636.5677.0496963.12
3USER000031956.210.1250.543.2441SilverDesktopDirect401.444563-1.5360140.09477432.28163.74962021.60
4USER000041110.390.29112.230.1950FreeMobileAds390.1739530.952862-0.44181013.1921.32374376.97
5USER00005139.980.24106.591.8950FreeDesktopAds581.0612570.617262-1.35576503.12201.45516182.22
6USER000061457.670.1374.201.7111SilverTabletAds890.996949-0.016475-0.17200931.82126.88206603.80
7USER000071654.760.40123.260.1071FreeMobileAds150.0953101.0251302.16592936.4012.32601848.90
8USER000081247.730.18104.380.3731FreeMobileEmail810.3148110.8917020.52622032.1638.62068454.78
9USER000091427.400.4854.322.3220FreeDesktopEmail261.199965-0.431074-0.75565216.72126.02241412.32
user_idnum_product_viewsavg_view_timeclick_through_rateavg_cart_valuepurchase_frequencynum_categories_browsedused_discountmembership_statusdevice_typereferral_channeldays_since_last_loginpurchase_frequency_logpca1pca2clusteractivity_scorevalue_scorerecency_value
9990USER099901829.640.2394.730.3110FreeMobileSocial70.2700270.147503-0.67992114.1429.3663663.11
9991USER099911145.630.3582.211.5581FreeMobileSocial670.9360930.3510901.31997133.85127.42555508.07
9992USER099921351.410.18117.132.9960GoldMobileSocial241.383791-0.218299-0.44696312.34350.21872811.12
9993USER09993152.130.1494.000.3951FreeMobileEmail720.3293040.552786-0.19358332.1036.66006768.00
9994USER099941358.800.2472.282.2950SilverMobileDirect391.190888-0.427960-0.23715013.12165.52122818.92
9995USER099952721.610.11104.110.5021SilverMobileEmail850.4054651.421833-1.08900032.9752.05508849.35
9996USER099961944.550.3476.882.5430FreeTabletAds61.264127-0.508272-0.62798016.46195.2752461.28
9997USER099971831.810.1093.131.0930SilverMobileSocial650.7371640.608601-1.49376801.80101.51176053.45
9998USER099981433.960.18103.761.6590SilverDesktopDirect710.9745600.878594-0.57652402.52171.20407366.96
9999USER099991278.880.08122.110.6961SilverMobileEmail60.524729-0.3718382.15086830.9684.2559732.66